Heuristic Optimization Methods Pareto Multiobjective Optimization

1. Heuristic Optimization MethodsPareto Multiobjective Optimization Patrick N. Ngatchou, Anahita Zarei, Warren L. J. Fox, and Mohamed A. El-Sharkawi C.-S. Shieh, EC, KUAS, Taiwan

2. 10.1 Introduction • The solution to multiobjective (MO) problems consists of sets of tradeoffs between objectives. • The goal of multiobjective optimization (MOO) algorithms is to generate these tradeoffs. • Exploring all these trade-offs is particularly important because it provides the system designer/operator with the ability to understand and weigh the different choices available to them. C.-S. Shieh, EC, KUAS, Taiwan

3. 10.1 Introduction (cont) • Solving MO problems has traditionally consisted of converting all objectives into a SO function. • This simple optimization process is no longer acceptable for systems with multiple conflicting objectives: System engineers may desire to know all possible optimized solutions of all objectives simultaneously. In the business world, it is known as a trade-off analysis. C.-S. Shieh, EC, KUAS, Taiwan

4. 10.1 Introduction (cont) • This chapter focuses on heuristic multiobjective optimization, particularly with population-based stochastic algorithms such as evolutionary algorithms. C.-S. Shieh, EC, KUAS, Taiwan

5. 10.2 Basic Principles • For illustration purposes, consider the hypothetical problem of determining, given a choice of transportation means, the most efficient of them based on distance covered in a day and energy used in the process. C.-S. Shieh, EC, KUAS, Taiwan

6. 10.2 Basic Principles (cont) C.-S. Shieh, EC, KUAS, Taiwan

7. 10.2.1 Generic Formulation of MO Problems C.-S. Shieh, EC, KUAS, Taiwan

8. 10.2.1 Generic Formulation of MO Problems (cont) C.-S. Shieh, EC, KUAS, Taiwan

9. 10.2.2 Pareto Optimality Concepts • The concepts of Pareto dominance and Pareto optimality. • A solution belongs to the Pareto set if there is no other solution that can improve at least one of the objectives without degrading any other objective. C.-S. Shieh, EC, KUAS, Taiwan

10. 10.2.2 Pareto Optimality Concepts (cont) • In the context of MOO, Pareto dominance is used to compare and rank decision vectors. • u dominating v in the Pareto sense means that F(u) is either better than or the same as F(v) for all objectives, and there is at least one objective function for which F(u) is strictly better than F(v). C.-S. Shieh, EC, KUAS, Taiwan

11. 10.2.2 Pareto Optimality Concepts (cont) • A solution a is said to be Pareto optimal if and only if there does not exist another solution that dominates it. • The set of all Pareto optimal solutions is called the Pareto optimal set. C.-S. Shieh, EC, KUAS, Taiwan

12. 10.2.2 Pareto Optimality Concepts (cont) C.-S. Shieh, EC, KUAS, Taiwan

13. 10.2.3 Objectives of Multiobjective Optimization • MOO consists of determining all solutions to the MO problem that are optimal in the Pareto sense. • Good solutions to a MO problem • (a) Minimize the distance between the approximation set generated by the algorithm and the Pareto front; • (b) Ensure a good distribution of solutions along the approximation set (uniform if possible); • (c) Maximize the range covered by solutions along each of the objectives. C.-S. Shieh, EC, KUAS, Taiwan

14. 10.2.3 Objectives of Multiobjective Optimization (cont) C.-S. Shieh, EC, KUAS, Taiwan

15. 10.3 Solution Approaches • Classic approaches, which have roots in the operations research and optimization theory fields, essentially consist of converting the MO problem into a SO problem, which then can be solved using traditional scalar optimization techniques. C.-S. Shieh, EC, KUAS, Taiwan

16. 10.3 Solution Approaches (cont) • Population-based algorithms such as evolutionary algorithms, particle swarm optimization, or ant colony optimization allow direct generation of trade-off curves in a single run. C.-S. Shieh, EC, KUAS, Taiwan

17. 10.3.1 Classic Methods • Classic methods were essentially techniques developed by the operations research community to address the problem of multicriteria decision making (MCDM). C.-S. Shieh, EC, KUAS, Taiwan

18. 10.3.1 Classic Methods (cont) • Given multiple objectives and preferential information about these objectives, the MO problem is converted into an SO problem by either aggregating the objective functions or optimizing the most important objective and treating the others as constraints. C.-S. Shieh, EC, KUAS, Taiwan

19. 10.3.1 Classic Methods (cont) • In the general case, and in order to generate an approximation to the nondominated front, all that is needed is to modify the aggregation parameters and solve the newly created SO problem. C.-S. Shieh, EC, KUAS, Taiwan

20. 10.3.1.1 Weighted Aggregation C.-S. Shieh, EC, KUAS, Taiwan

21. 10.3.1.2 Goal Programming C.-S. Shieh, EC, KUAS, Taiwan

22. 10.3.1.3 e-Constraint C.-S. Shieh, EC, KUAS, Taiwan

23. 10.3.1.4 Discussion on Classic Methods • Classic methods attempt to ease the decision-making process by incorporating a priori preferential information from the DM and are geared toward finding the single solution representing the best compromise solution. C.-S. Shieh, EC, KUAS, Taiwan

24. 10.3.2 Intelligent Methods C.-S. Shieh, EC, KUAS, Taiwan

25. 10.3.2.1 Background • Meta-heuristics are a practical way to generate acceptable solutions, even though they cannot guarantee optimality. • Another advantage is the ability to incorporate problem-specific knowledge to improve the quality of the solutions. C.-S. Shieh, EC, KUAS, Taiwan

26. 10.3.2.1 Background (cont) C.-S. Shieh, EC, KUAS, Taiwan

27. 10.3.2.2 Structure of Population-Based MOO Solvers • The general structure of EA-based MO solvers is similar to the one used for SOO. • Fitness assignment controls convergence (i.e., how to guide the population to nondominated solutions). • To prevent premature convergence to a region of the front, diversity mechanisms such as niching are included in the determination of an individual’s fitness. C.-S. Shieh, EC, KUAS, Taiwan

28. 10.3.2.2 Structure of Population-Based MOO Solvers (cont) • A form of elitism is applied to prevent the deterioration problem whereby nondominated solutions may disappear from one generation to the next. C.-S. Shieh, EC, KUAS, Taiwan

29. 10.3.2.2 Structure of Population-Based MOO Solvers (cont) C.-S. Shieh, EC, KUAS, Taiwan

30. 10.3.2.2.1 Fitness Assignment • There are three methods of fitness assignment: aggregation-based, criterion-based, and Pareto-based. • Aggregation-based assignment consists in evaluating the fitness of each individual based on a weighted aggregation of the objectives. C.-S. Shieh, EC, KUAS, Taiwan

31. 10.3.2.2.1 Fitness Assignment (cont) • To explore the different parts of the Pareto front, they apply systematic variation of the aggregation weights. • An example of criterion-based assignment is Schaffer’s vector-evaluated genetic algorithm (VEGA). • At each generation, the population is divided into as many equal-size subgroups as there are objectives, and the fittest individuals for each objective function are selected C.-S. Shieh, EC, KUAS, Taiwan

32. 10.3.2.2.1 Fitness Assignment (cont) C.-S. Shieh, EC, KUAS, Taiwan

33. 10.3.2.2.1 Fitness Assignment (cont) • Pareto-based fitness assignment is the most popular and efficient technique. Here, Pareto-dominance is explicitly applied in order to determine the probability of replication of an individual. • The multiobjective genetic algorithm (MOGA) is an algorithm implementing Pareto-based fitness assignment C.-S. Shieh, EC, KUAS, Taiwan

34. 10.3.2.2.1 Fitness Assignment (cont) C.-S. Shieh, EC, KUAS, Taiwan

35. 10.3.2.2.2 Diversity • In conjunction with fitness assignment mechanism, an appropriate niching mechanism is necessary to prevent the algorithm from converging to a single region of the Pareto front • In the MOGA algorithm discussed earlier, an objective space density-based fitness sharing is applied after population ranking C.-S. Shieh, EC, KUAS, Taiwan

36. 10.3.2.2.3 Elitism • In EA-based solvers, an elitist strategy refers to a mechanism by which the fittest individuals found during the evolutionary search are always copied to the next generation. C.-S. Shieh, EC, KUAS, Taiwan

37. 10.3.2.2.3 Elitism (cont) • In SPEA, a repository or external archive is used to maintain nondominated solutions and is updated at each generation if better nondominated solutions are found. C.-S. Shieh, EC, KUAS, Taiwan

38. 10.3.2.3 Common Population-Based MO Algorithms C.-S. Shieh, EC, KUAS, Taiwan

39. 10.3.2.4 Discussion on Modern Methods C.-S. Shieh, EC, KUAS, Taiwan

40. 10.4 Performance Analysis C.-S. Shieh, EC, KUAS, Taiwan

41. 10.4.1 Objective of Performance Assessment C.-S. Shieh, EC, KUAS, Taiwan

42. 10.4.2 Comparison Methodologies C.-S. Shieh, EC, KUAS, Taiwan

43. 10.4.2.1 Quality Indicators C.-S. Shieh, EC, KUAS, Taiwan

44. 10.4.2.2 Attainment Function Method C.-S. Shieh, EC, KUAS, Taiwan

45. 10.4.2.3 Dominance Ranking C.-S. Shieh, EC, KUAS, Taiwan

46. 10.5 Conclusions C.-S. Shieh, EC, KUAS, Taiwan